To study the impact of different strategies for dealing with the available LF angular information, t-SNE [1]} is used again to plot the feature spaces produced by the different strategies, thus showing the different discrimination ability of the various LF angular features in a two dimensional space. Figure REF  offers a visualization of the features that are used as inputs to the classifier, when four different strategies are used to deal with the angular information, notably: i) only the 2D central SA image is used, thus ignoring the available angular information (Figure REF -a) ; ii) the concatenated features extracted from all the views are used (without capsule network) to feed the classifier (FigureREF -b); iii) LSTM RNN [2]} is used to exploit the angular information (FigureREF -c); and iv) capsule network, as proposed in this paper, is used to exploit the angular information (Figure REF -d). To make the visualisation legible, the t-SNE analysis is only performed for the first 10 LFFW subjects using the cross-pose and expression face recognition protocol. The t-SNE plots show that the proposed CapsField solution forms denser and more effective clusters that can facilitate distinguishing between the various classes; this validates the superiority of the capsule network in learning angular information.
